What does this research mean for the field?

A reinforcement learning approach effectively determines optimal departure times for customers in virtual queue systems with travel times and unknown wait distributions, generalizing well across different underlying data distributions. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Develop a reinforcement learning framework that determines optimal departure times for customers navigating virtual queue systems with travel delays and reservation deposits under unknown wait-time distributions.

September 2, 2026Applied Operations and AnalyticsOpen Access

Learning when to leave: reinforcement learning for service systems with virtual queues and reservation deposits

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Develop a reinforcement learning framework that determines optimal departure times for customers navigating virtual queue systems with travel delays and reservation deposits under unknown wait-time distributions.
Formulated an exploration-exploitation reinforcement learning framework to optimize customer departure timing without requiring prior knowledge of queue wait-time probability distributions.
Trained the model using gamma distributions and benchmarked departure decisions against an omniscient oracle across various alternate probability distributions.
Evaluated the algorithmic performance using real-world operational data from a bike-sharing system.
The reinforcement learning approach closely matches the decision performance of an oracle possessing complete distributional knowledge.
Policies trained on gamma distributions transfer effectively to test scenarios governed by alternative probability distributions.
Evaluation on bike-sharing data demonstrates reliable and robust departure timing under empirical travel and queue uncertainty.

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Implication

Computational study demonstrates effective departure timing using reinforcement learning in virtual queue service systems, highlighting robust decision-making under unknown wait-time distributions.

Cite This Study

Karia et al. (2026) studied this question.

synapsesocial.com/papers/6a97e28dc562ede874ec6c44 https://doi.org/10.1080/29966892.2026.2722521

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Implication

Computational study demonstrates effective departure timing using reinforcement learning in virtual queue service systems, highlighting robust decision-making under unknown wait-time distributions.

Learning when to leave: reinforcement learning for service systems with virtual queues and reservation deposits

Key Points

Discussion

Implication

Cite This Study

Learning when to leave: reinforcement learning for service systems with virtual queues and reservation deposits

Key Points

Discussion

Implication

Cite This Study

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